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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Optimal Endmember-Based Super-Resolution Land Cover Mapping
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Optimal Endmember-Based Super-Resolution Land Cover Mapping

机译：基于最终成员的最优超分辨率土地覆盖图

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摘要

Super-resolution mapping (SRM) aims to determine the spatial distribution of the land cover classes contained in the area represented by mixed pixels to obtain a more appropriate and accurate map at a finer spatial resolution than the input remotely sensed image. The image-based SRM models directly use the observed images as input and can mitigate the uncertainty caused by class fraction errors. However, existing image-based SRM models always adopt a fixed set of endmembers used in the entire image, ignoring the spatial variability and spectral uncertainty of endmembers. To address this problem, this letter proposed an optimal endmember-based SRM (OESRM) model, which considers the spatial variations in endmembers, and determines the best-fit one for each coarse resolution pixel using the spectral angle and the spectral distance as the spectral similarity indexes. A Sentinel-2A and a Landsat-8 multispectral images were used to analyze the performance of OESRM, by comparing with three other SRM methods which adopt a fixed endmember set or multiple endmember sets. The results showed that OESRM generated resultant land cover maps with more spatial detail, and reduced the confusion between land cover classes with similar spectral features. The proposed OESRM model produced the results with the highest overall accuracy in both experiments, showing its effectiveness in reducing the effect of endmember uncertainty on SRM.
机译：超分辨率映射（SRM）的目的是确定包含在混合像素表示的区域中的土地覆盖类别的空间分布，以便以比输入的遥感图像更精细的空间分辨率获得更合适，更准确的地图。基于图像的SRM模型直接将观察到的图像用作输入，并可以减轻由类别分数错误引起的不确定性。但是，现有的基于图像的SRM模型始终采用在整个图像中使用的固定端构件集，而忽略了端构件的空间变异性和光谱不确定性。为了解决这个问题，这封信提出了一个最佳的基于端构件的SRM（OESRM）模型，该模型考虑了端构件的空间变化，并使用光谱角度和光谱距离作为光谱，为每个粗分辨率像素确定了最佳拟合模型相似性指标。通过与采用固定端成员集或多个端成员集的其他三种SRM方法进行比较，使用Sentinel-2A和Landsat-8多光谱图像来分析OESRM的性能。结果表明，OESRM生成了具有更多空间细节的合成土地覆盖图，并减少了具有相似光谱特征的土地覆盖类别之间的混淆。所提出的OESRM模型在两个实验中产生的结果都具有最高的整体准确度，显示了其在减少端部不确定性对SRM影响方面的有效性。

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